The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda

A large literature describes how local risk sharing networks can help individuals smooth consumption in the face of idiosyncratic economic shocks. However, when an entire community faces a large covariate shock, and when the transaction costs of transfers are high, these risk sharing networks are likely to be less effective. In this paper, we document how a new technology – mobile phones – reduces transaction costs and enables Rwandans to share risk quickly over long distances. We examine a comprehensive database of person-to-person transfers of mobile airtime and find that individuals send this rudimentary form of “mobile money” to friends and family affected by natural disasters. Using the Lake Kivu earthquake of 2008 to identify the effect of a large covariate shock on interpersonal transfers, we estimate that a current-day earthquake would result in the transfer of between $22,000 and$30,000 to individuals living near the epicenter. We further show that the pattern of transfers is most consistent with a model of reciprocal risk sharing, where transfers are determined by past reciprocity and geographical proximity, rather than one of pure charity or altruism, in which transfers would be expected to be increasing in the wealth of the sender and decreasing in the wealth of the recipient.Risk Sharing; Mobile Phones; Mobile Money; Information and communications technologies; Development; Earthquakes; Rwanda; Africa.

Events in social networks : a stochastic actor-oriented framework for dynamic event processes in social networks

Interactions between people are ubiquitous. When people make phone calls, transfer money, connect on social network sites, or visit each other, these actions can be collected as dyadic, directed, relational events. Each of those events can be understood as driven by multiple individual decisions that at least partially involve rational considerations. This book aims at developing models that allow to understand individual event decisions in the context of large social networks

Examining the components of children's peer liking as antecedents of school adjustment

Children’s social interactions with their peers influence their psychosocial adjustment; consequently, the relationship between class-wide peer liking, same-gender peer liking, and school adjustment was explored in two age groups. Peer liking was analysed using the social relations model (SRM). In Study 1, 205 children (103 female and 102 male, Mage = 7.15, SD = 7 months) completed measures of peer liking and school adjustment, and teachers completed the Short-Form TRSSA. In Study 2, 197 children (98 female and 90 male, Mage = 9.87, SD = 5.9 months) completed measures of peer liking and school adjustment. Both studies yielded evidence of reciprocal liking and individual differences in the ratings of liking awarded to, and elicited from, both peer groups. Multigroup path analysis, with groups created according to gender, revealed that elements of liking predicted different aspects of school adjustment with some variation according to age and gender. Together, these findings suggest that the SRM can be used to examine peer liking and underscore the importance of children’s peers for school adjustment

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

Cultural proximity and trade

Cultural proximity increases bilateral trade flows through a trade-cost and a bilateral-affinity (preferences) channel. Conventional measures of cultural proximity, such as common language, common religion, etc., do not allow to separately quantify those channels empirically. We argue that quality-adjusted Eurovision Song Contest (ESC) scores can be used as dyadic, time-variant information on European countries' cultural proximity. Assuming that the tradecost related component of cultural proximity is time-invariant, in a gravity model of bilateral trade, the time dimension of the ESC data allows to identify the preferences effect. The validity of our identification strategy can be tested by exploiting the lack of systematic reciprocity in ESC scores. While we find robust evidence for a sizable preferences effect, the impact of cultural proximity on trade runs largely through the cost effect. --international trade,gravity equation,cultural prox imity,identification